Datasets:

TabBioMed
/

lymphography

license: cc-by-4.0
pretty_name: lymphography
task_categories:
  - tabular-classification
tags:
  - tabular
  - biomedical
  - benchmark
  - TabBioMed
  - clinical-&-healthcare
  - binary
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: fold_0_train
        path: data/fold_0/train.parquet
      - split: fold_0_validation
        path: data/fold_0/val.parquet
      - split: fold_0_test
        path: data/fold_0/test.parquet
      - split: fold_1_train
        path: data/fold_1/train.parquet
      - split: fold_1_validation
        path: data/fold_1/val.parquet
      - split: fold_1_test
        path: data/fold_1/test.parquet
      - split: fold_2_train
        path: data/fold_2/train.parquet
      - split: fold_2_validation
        path: data/fold_2/val.parquet
      - split: fold_2_test
        path: data/fold_2/test.parquet
      - split: fold_3_train
        path: data/fold_3/train.parquet
      - split: fold_3_validation
        path: data/fold_3/val.parquet
      - split: fold_3_test
        path: data/fold_3/test.parquet
      - split: fold_4_train
        path: data/fold_4/train.parquet
      - split: fold_4_validation
        path: data/fold_4/val.parquet
      - split: fold_4_test
        path: data/fold_4/test.parquet

lymphography

Part of TabBioMed (TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning), a benchmark of 94 curated biomedical tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed.

Lymphography (OpenML ID 10), restricted to metastases (1; 81 samples) versus malignant lymph (0; 61 samples). The two normal and four fibrosis cases are excluded. All 18 source features and the source row order are retained. Evaluation uses five stratified outer folds and three stratified inner folds for hyperparameter selection; preprocessing is fitted within training partitions.

Domain	Task	Samples	Features	Classes
Clinical & Healthcare	binary	142	18	2

Splits. 5 stored outer cross-validation folds (data/fold_<i>/), each with train/val/test files (fold 0: 98/15/29 rows). Every row appears in exactly one outer test fold. HPO in the paper uses inner folds drawn from each outer non-test pool.

Files

data/**.parquet: one row per sample. row_idx is the row index in the original TabBioMed array; f_<i>[_<name>] are the features (in order); target (or target_<j> for multi-target tasks) is the label. Categorical feature indices (0-based, among the features): [0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16]. Missing values are stored as nulls.
metadata.json: TabBioMed task metadata (task type, categorical indices, source, description).
feature_names.json: original feature names, in column order.
sample_metadata.csv: per-row provenance (identifiers, source annotations); not features.

from datasets import load_dataset
ds = load_dataset("TabBioMed/lymphography")

A Croissant (1.1) description with Responsible-AI fields is distributed with the TabBioMed release.

Source and license

Source: OpenML_Dataset_ID_10

This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also cite and respect the terms of the original source.

Citation

@inproceedings{tabbiomed2026,
  title     = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning},
  author    = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}